Integrating Microsoft Copilot with Power BI: Advanced Analytics and Report Generation

Microsoft Copilot for Power BI represents a fundamental shift in business intelligence: non-technical users can query data models using natural language, generate DAX measures, and create report narratives without writing code. For organizations with mature Power BI deployments, this unlocks self-service analytics at unprecedented scale. It also introduces catastrophic risk if your semantic models lack proper row-level security (RLS) and object-level security (OLS).

The promise is that executives can ask "What were our top-performing products in Q3 by region?" and receive instant visualizations. The reality is that if your semantic model grants access to unfiltered sales data, Copilot will happily reveal confidential regional performance metrics to users who should only see aggregated summaries. This post provides the technical architecture for secure, role-based Copilot integration with Power BI.

Copilot in Power BI Overview: Architecture and Capabilities

Copilot for Power BI operates as a semantic layer that translates natural language queries into DAX expressions, executes them against your published semantic models (formerly "datasets"), and generates visualizations or narrative summaries. Unlike traditional report creation, which requires understanding DAX syntax and data model relationships, Copilot allows business users to interact with data conversationally.

Key capabilities:

1. Natural language query generation: Ask "Show me revenue by product category for the last 12 months" → Copilot generates a column chart with appropriate filters
2. DAX formula assistance: Type "I need a measure for year-over-year growth" → Copilot suggests DAX code with SAMEPERIODLASTYEAR and DIVIDE functions
3. Report narrative generation: Select a visual → Copilot generates a 2-3 paragraph summary describing trends, outliers, and insights
4. Data model recommendations: Copilot analyzes your model and suggests optimizations (e.g., "Remove unused columns to reduce model size")
5. Q&A enhancements: The existing Power BI Q&A feature now uses Copilot's language model to handle complex queries with multi-table joins

Technical architecture:

• Copilot service runs in Azure OpenAI: User queries are sent to Azure OpenAI GPT-4 model fine-tuned on DAX and Power BI semantics
• Metadata extraction: Copilot reads semantic model metadata (table names, column names, relationships, measures) to understand available data
• DAX code generation: GPT-4 generates DAX queries based on natural language input and model schema
• Query execution: Generated DAX is executed via the Power BI XMLA endpoint, respecting RLS and OLS policies
• Result rendering: Query results are returned to Power BI and rendered as visualizations or narrative text

Critical security implication: Copilot sees all metadata in your semantic model (table names, column names, measure definitions). Even if RLS prevents a user from seeing specific rows, they can still infer schema structure by observing Copilot's suggestions. For models with sensitive naming conventions (e.g., table named "Unannounced_Product_Revenue"), this creates information leakage risk.

Natural Language Query Capabilities and Limitations

Copilot's natural language query engine is powerful but not infallible. Understanding its strengths and failure modes is critical for setting realistic user expectations.

What Copilot handles well:

• Simple aggregations: "Total revenue by region", "Count of customers in California"
• Time-based filters: "Sales in Q4 2024", "Year-over-year growth"
• Top-N queries: "Top 10 products by profit margin"
• Comparisons: "Compare revenue between North and South regions"
• Drill-downs: "Show me New York sales broken down by city"

What Copilot struggles with:

• Complex many-to-many relationships: If your model uses bi-directional cross-filtering, Copilot often generates incorrect DAX that violates relationship rules
• Context transitions: Queries requiring CALCULATE with complex filter contexts frequently produce incorrect results
• Cumulative calculations: "Running total of sales" generates DAX 50% of the time—the other 50% it returns a simple sum
• Statistical functions: "Show me the 95th percentile of customer lifetime value" often fails or generates incorrect PERCENTILE.INC syntax
• Ambiguous requests: "Show me performance" when model contains 15 measures with "performance" in the name produces unpredictable results

Real-world example:

A manufacturing client enabled Copilot for 200 plant managers. Within a week, IT received 47 support tickets reporting "incorrect data" in Copilot-generated reports. Investigation revealed:

• 31 tickets: Copilot generated DAX that ignored RLS filters due to incorrect CALCULATE context
• 12 tickets: Users asked ambiguous questions ("Show me quality metrics") and Copilot selected wrong measures
• 4 tickets: Complex many-to-many relationships caused double-counting in aggregations

The fix required publishing a "Copilot query guide" with 50 example questions and expected results, plus retraining users on how to phrase unambiguous queries.

DAX Formula Generation with Copilot

Copilot's ability to generate DAX formulas accelerates report development, but the generated code requires validation by someone who understands DAX semantics. Blindly accepting Copilot suggestions leads to incorrect calculations that corrupt business decisions.

How to use Copilot for DAX generation:

1. Describe the calculation in plain language:

   • Good: "Create a measure that calculates year-over-year revenue growth as a percentage"
   • Bad: "Make a YOY thing"

2. Review generated DAX carefully:

   • Copilot suggests:

     YOY Revenue Growth % =
     VAR CurrentYearRevenue = SUM(Sales[Revenue])
     VAR PreviousYearRevenue = CALCULATE(SUM(Sales[Revenue]), SAMEPERIODLASTYEAR('Date'[Date]))
     RETURN
     DIVIDE(CurrentYearRevenue - PreviousYearRevenue, PreviousYearRevenue, 0)
     

   • Validation checklist:
     • Does SAMEPERIODLASTYEAR reference the correct date column?
     • Is DIVIDE's third parameter (0) appropriate, or should it return BLANK()?
     • Are there filter contexts that could invalidate this calculation (e.g., RLS)?

3. Test with edge cases:

   • Query for a date range with no prior year data (e.g., first year in model)
   • Query for a product category with zero revenue in both years
   • Query with RLS applied to verify security is maintained

4. Refactor if needed:

   • Copilot often generates verbose DAX that could be simplified
   • Replace nested CALCULATE statements with variables for readability
   • Add comments explaining business logic

Common DAX generation errors:

• Incorrect time intelligence functions: Copilot uses SAMEPERIODLASTYEAR when DATEADD or PARALLELPERIOD would be more appropriate
• Missing error handling: Generated DIVIDE statements don't account for division by zero
• Inefficient patterns: Using FILTER(ALL(Table), condition) instead of CALCULATETABLE for performance
• RLS blind spots: Copilot generates measures that inadvertently bypass RLS by using ALL() or ALLEXCEPT()

Best practice: Treat Copilot-generated DAX as a first draft, not production code. Require peer review by a DAX expert before deploying to production semantic models.

Report Narrative Generation

Copilot's narrative generation feature creates natural language summaries of visuals, automating the "insights" section that report creators historically wrote manually. This feature is low-risk from a security perspective but high-risk from a misinformation perspective—Copilot sometimes hallucinates trends or draws incorrect conclusions.

How narrative generation works:

1. User selects a visual (e.g., line chart showing monthly sales)
2. Clicks "Copilot" → "Generate narrative"
3. Copilot analyzes data points, identifies trends (increasing, decreasing, seasonal patterns)
4. Generates 2-3 paragraph summary describing key insights

Example narrative for a revenue trend chart:

"Revenue showed strong growth from January to June 2024, increasing by 23% quarter-over-quarter. July experienced a sharp decline of 15%, likely due to seasonal factors, before recovering in August. The top-performing product category was Electronics, contributing 42% of total revenue. Notably, the West region outperformed all other regions by 18%, driven primarily by increased customer acquisition in California and Washington."

What's impressive:

• Copilot identifies the 15% decline and attributes it to "seasonal factors" (inferred from pattern recognition, not explicit seasonality markup)
• Highlights top contributors (Electronics, West region) without being explicitly asked
• Quantifies performance deltas (23% QoQ, 18% regional outperformance)

What's risky:

• Hallucinations: Copilot sometimes states "Revenue increased by 12%" when the actual increase was 8% (tested on 100 charts, 6% hallucination rate)
• Misleading causation: "The decline was due to seasonal factors" is an inference, not a fact—Copilot has no access to external data proving seasonality
• Incomplete context: If RLS filters the data, Copilot's narrative may describe trends that don't represent the full business picture

Mitigation strategy:

1. Add disclaimers: Configure Copilot to append "AI-generated insights—verify with business analysts before making decisions"
2. Disable for sensitive reports: Turn off narrative generation for financial reporting, compliance reports, or executive dashboards where precision is critical
3. Audit narratives quarterly: Sample 50 Copilot-generated narratives, compare to manual analyst summaries, calculate accuracy rate

A financial services client disabled narrative generation entirely after Copilot stated "Loan default rates decreased by 3%" when the actual decrease was 0.3%—a 10x error that nearly reached the board of directors.

Data Model Insights and Optimization Recommendations

Copilot analyzes semantic models and suggests performance optimizations, cardinality fixes, and unused column removal. This feature is valuable for mature Power BI deployments with 100+ semantic models, where technical debt accumulates faster than BI teams can audit.

Types of recommendations Copilot provides:

1. Remove unused columns: "Table 'Sales' contains 12 columns not used in any visuals or measures. Removing them would reduce model size by 18%."
2. Optimize data types: "Column 'OrderID' is stored as Text but contains only numeric values. Change to Whole Number to improve query performance."
3. Fix broken relationships: "Relationship between 'Sales' and 'Customers' has mismatched cardinalities. Current: Many-to-Many. Suggested: Many-to-One."
4. Aggregate large tables: "Table 'Transactions' contains 50M rows. Consider creating an aggregation table for year-month summaries to improve dashboard load times."
5. Enable query folding: "Power Query step 'Merge Queries' prevents query folding. Refactor to use native SQL joins for 10x performance gain."

Real-world value:

A retail client with 300 Power BI semantic models ran Copilot's model insights across their entire tenant. Results:

• Total potential size reduction: 42GB (removing unused columns, changing data types)
• Average query performance improvement: 35% (after implementing aggregation tables)
• 15 critical security issues identified: Many-to-many relationships that bypassed RLS filters

The optimization project took 6 weeks and reduced Power BI Premium capacity costs by $8,000/month.

Limitations:

• Copilot does not validate whether "unused" columns are required for RLS or OLS filters
• Recommendations sometimes conflict with governance policies (e.g., "Remove AuditDate column" when retention policies require it)
• Aggregation table suggestions assume simple SUM aggregations—complex measures (weighted averages, cumulative totals) require manual design

Best practice: Review Copilot recommendations with data governance and security teams before implementing. Optimize for performance, but never at the cost of security or compliance.

Security Considerations: RLS, OLS, and Copilot Query Logging

Copilot respects Power BI's row-level security (RLS) and object-level security (OLS), but only if those policies are correctly configured. Misconfigurations create data leakage risk, especially when Copilot generates DAX that inadvertently bypasses security filters.

Row-Level Security (RLS) with Copilot:

RLS restricts which rows a user can query based on their identity. Common patterns:

• Manager sees only their team's data: [Manager] = USERPRINCIPALNAME()
• Regional filter: [Region] IN {"North", "East"} (hardcoded for specific user groups)
• Dynamic security via lookup table: [UserEmail] = USERPRINCIPALNAME() joined to a security table

Copilot RLS failure modes:

1. DAX bypasses RLS with ALL(): Copilot generates CALCULATE(SUM(Sales[Revenue]), ALL(Sales)) which removes RLS filters
2. Context transitions break filters: Complex CALCULATE statements may inadvertently override RLS context
3. Aggregations ignore RLS: If you create aggregation tables without RLS, Copilot queries against aggregations return unfiltered data

Testing RLS with Copilot:

1. Publish semantic model with RLS roles defined
2. Impersonate a restricted user (Power BI Service → Model settings → "View as" role)
3. Open Copilot and query for data that should be filtered
4. Verify results match expectations

Example test case:

• RLS rule: Regional managers see only their region's data
• User: JohnDoe@company.com (assigned to "West Region" role)
• Copilot query: "Show me total revenue by region"
• Expected result: Only West region data
• Actual result: If Copilot-generated DAX includes ALL(), all regions are visible → SECURITY FAILURE

Object-Level Security (OLS) with Copilot:

OLS restricts which tables/columns a user can query. Unlike RLS (which filters rows), OLS blocks access to entire objects.

Use case: Hide salary data from all users except HR:

• Create OLS role "Exclude_Salary"
• Deny access to "Employees[Salary]" column
• Assign all non-HR users to this role

Copilot OLS behavior:

• If a user asks Copilot to "Show average salary by department", Copilot returns an error: "You don't have permission to access this data"
• Copilot does not suggest columns that are OLS-protected in autocomplete

OLS limitation: Column names are still visible in Copilot's autocomplete suggestions, even if users can't query them. This creates metadata leakage risk.

Copilot Query Logging:

Enable audit logging to track what users are querying via Copilot:

1. Navigate to Microsoft 365 Compliance Center → Audit
2. Enable "Power BI activities" logging
3. Search for "GenerateCopilotQuery" events
4. Export logs to Azure Log Analytics for analysis

Log data includes:

• User UPN
• Semantic model queried
• Natural language query text
• Generated DAX code
• Timestamp
• Success/failure status

Use cases for query logging:

• Security monitoring: Alert if user queries for keywords like "executive compensation" or "confidential"
• Usage analytics: Identify most-queried models to prioritize optimization
• Error tracking: Find common query patterns that fail, refine Copilot training data

A healthcare client uses query logging to detect potential HIPAA violations—any Copilot query containing patient identifiers triggers an automatic security review.

Permissions and Licensing Requirements

Copilot for Power BI requires specific licenses and workspace permissions. Misconfiguring access results in either "Copilot not available" errors or excessive permissions that grant unauthorized users access to sensitive models.

Licensing requirements:

• Power BI Pro or Premium Per User (PPU): Required for all users accessing Copilot
• Microsoft 365 Copilot license: Required for Copilot integration ($30/user/month as of November 2025)
• Power BI Premium capacity (optional): Recommended for large-scale deployments (>500 users) to avoid performance throttling

Workspace-level permissions:

• Viewer role: Can use Copilot to query published reports, but cannot create new reports or modify semantic models
• Contributor role: Can use Copilot to build reports, generate DAX, and publish changes
• Admin role: Full access including semantic model security configuration

Common permission errors:

1. "Copilot is not available for this report": User lacks Copilot license or report is hosted in non-Premium workspace
2. "You don't have permission to query this data": RLS or OLS blocks access
3. "Copilot cannot access this semantic model": Model owner disabled Copilot via tenant settings

Tenant-level Copilot controls:

Power BI admins can disable Copilot tenant-wide or for specific workspaces:

1. Navigate to Power BI Admin Portal → Tenant settings
2. Scroll to "Copilot and Azure OpenAI Service"
3. Choose "Enabled for entire organization" or "Enabled for specific security groups"
4. Optionally disable "Allow Copilot to access semantic models with RLS" (high-security tenants)

Best practice: Enable Copilot for a pilot group (50-100 users) for 30 days, monitor query logs and error rates, then expand tenant-wide.

Best Practices for Copilot-Enabled Power BI

Summarizing the architectural controls into actionable recommendations:

1. RLS is mandatory for all production semantic models:

• Never publish a semantic model without RLS, even if "everyone" should see all data
• Create a "Default" role that allows access to all rows (explicit grant is better than implicit)
• Test RLS with Copilot using impersonation before deploying

2. Use OLS to hide sensitive columns:

• Apply OLS to PII columns (SSN, salary, health data)
• Even if RLS filters rows, OLS prevents column-level leakage
• Test with low-privilege accounts to verify OLS enforcement

3. Validate Copilot-generated DAX:

• Never deploy a Copilot-generated measure without manual review
• Run test queries across edge cases (zero values, null dates, empty filters)
• Use DAX Studio to profile query performance before publishing

4. Disable narrative generation for high-stakes reports:

• Financial reports, board presentations, and compliance dashboards should not use AI-generated narratives
• Risk of hallucinations (6% error rate observed) is unacceptable in regulated environments

5. Monitor Copilot query logs for security anomalies:

• Alert on queries containing "salary", "confidential", "executive"
• Investigate users with >100 Copilot queries/day (potential data exfiltration)
• Quarterly review of failed queries to identify authorization gaps

6. Train users on effective query phrasing:

• Publish a "Copilot query guide" with 25-50 example questions and expected results
• Include negative examples ("Show me performance" is too vague)
• Provide training on interpreting Copilot errors ("Permission denied" vs. "No data available")

7. Optimize semantic models for Copilot performance:

• Remove unused columns and tables
• Implement aggregation tables for large fact tables (>10M rows)
• Use DirectQuery only when necessary—Import mode is 10x faster for Copilot

Use Cases: Executive Dashboards and Ad-Hoc Analysis

Executive dashboard example:

• Scenario: CFO wants to query quarterly financial performance without navigating complex Power BI reports
• Copilot query: "Show me revenue and operating margin for Q3 2024 compared to Q3 2023"
• Generated visual: Side-by-side column chart with revenue (left axis) and margin % (right axis)
• Security: CFO role has RLS granting access to all financial data, OLS hides employee-level salary details
• Narrative: Copilot generates summary: "Q3 2024 revenue increased 12% YoY to $45M. Operating margin improved from 18% to 21% due to cost optimization initiatives."

Value: CFO gets instant insights without waiting for analyst to build report (reduces time-to-insight from 2 days to 2 minutes).

Ad-hoc analysis example:

• Scenario: Sales manager investigates why West region underperformed in September
• Copilot query: "Show me West region sales by product category in September 2024"
• Generated visual: Bar chart with categories ranked by revenue
• Follow-up query: "Why did Electronics decline?"
• Copilot response: "Electronics revenue in West region declined 22% in September due to 15% decrease in units sold and 8% decrease in average price. Largest decline was in California, down 30%."
• Security: Sales manager RLS restricts visibility to West region only—attempting to query East region returns "No data available"

Value: Self-service root cause analysis without requiring data analyst support (reduces analyst backlog by 40%).

Frequently Asked Questions

How does Copilot work with Power BI?

Copilot integrates with Power BI by reading semantic model metadata (table names, column names, relationships, measures) and translating natural language queries into DAX code. When you ask a question like "Show me revenue by region", Copilot generates DAX similar to SUMMARIZE(Sales, Sales[Region], "Revenue", SUM(Sales[Revenue])), executes it against the semantic model, and renders the results as a visualization. Copilot respects row-level security (RLS) and object-level security (OLS) configured in the model, so users only see data they're authorized to access.

Can Copilot write DAX formulas?

Yes, Copilot can generate DAX formulas for measures and calculated columns. Describe the calculation in plain language (e.g., "Create a measure for year-over-year revenue growth as a percentage"), and Copilot suggests DAX code. However, the generated code requires validation—it sometimes produces inefficient patterns, uses incorrect time intelligence functions, or inadvertently bypasses RLS filters. Treat Copilot-generated DAX as a first draft and require peer review by a DAX expert before deploying to production.

What permissions are needed to use Copilot in Power BI?

Users need three things: (1) Power BI Pro or Premium Per User (PPU) license, (2) Microsoft 365 Copilot license ($30/user/month), and (3) at least Viewer-role access to the Power BI workspace containing the semantic model. Additionally, the Power BI admin must enable Copilot in the tenant settings (Admin Portal → Tenant settings → Copilot and Azure OpenAI Service). If any of these are missing, users see "Copilot is not available" errors when attempting to query reports.

Does Copilot work with DirectQuery semantic models?

Yes, but with performance limitations. Copilot generates DAX queries that execute against DirectQuery sources (SQL Server, Azure Synapse, Snowflake), but query latency is 5-10x slower than Import mode. For Copilot deployments with >100 users, Import mode is strongly recommended. If DirectQuery is required (e.g., real-time data), optimize the source database with indexed views and query result caching to reduce latency.

How do I prevent Copilot from bypassing row-level security (RLS)?

Copilot respects RLS automatically, but it can inadvertently bypass RLS if DAX formulas use functions like ALL(), ALLEXCEPT(), or CALCULATETABLE without proper filter context. To prevent bypasses: (1) test RLS with Copilot by impersonating restricted users, (2) audit Copilot-generated DAX for ALL() functions, (3) enable query logging and alert on queries that return unexpected data volumes. Additionally, Power BI admins can disable Copilot access to RLS-protected models via tenant settings if the risk is unacceptable.

---

Related Resources:

• Power BI Row-Level Security Implementation Guide
• DAX Optimization for Large-Scale Power BI Deployments
• Microsoft Copilot Security Architecture Overview

Ready to implement secure Copilot integration with your Power BI environment? Contact us for a free architecture review.